Neural Bayesian Filtering
Neural Bayesian Filtering (NBF) is a method to track complex, shifting beliefs in partially observable systems using deep generative models. It maps belief states into fixed-length embedding vectors, allowing a learned network to efficiently sample and update these states. This combines classical filtering with deep learning to handle multimodal distributions without needing massive particle sets.